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Eugene
Agichtein
Assistant Professor
Intelligent Information Access Lab,
Mathematics & Computer Science Department, Emory University
Other affiliations: |
Data and Information Management Group
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Linguistics
| Computational and Life
Sciences
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CIKM 2008
Workshop on
Searching Social Media
(SSM
2008)
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SIGIR 2008 Workshop on
Information Retrieval in Advertising
(IRA
2008)
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Microsoft Research
"Beyond Search" award: Inferring Commercial Query Intent (Microsoft
press release) |
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I
direct the
Intelligent Information
Access Lab. We work on
information retrieval and text and data mining, including:
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Large-scale text mining,
information extraction,
and question answering over the web, scientific literature,
and social media
•
Mining
user behavior and
interactions in web search and online communities
Publications
(from
DBLP,
with citations from
Google
Scholar)
CV
(June 2008) |
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Selected Professional Service |
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Co-Chair,
CIKM
2008 Workshop on Searching Social Media
(SSM
2008)
Co-Chair,
SIGIR
2008
Workshop on Information Retrieval in Advertising
(IRA
2008)
Tutorials
Co-Chair,
Annual Meeting of the
Association for Computational Linguistics
(ACL
2008)
Senior Program
Committee member:
ACM SIGIR
International Conference on Information Retrieval
(SIGIR 2008)
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Program Committees:
WSDM 2009,
ECIR 2009,
KDD 2008, EMNLP 2008,
ICDM 2008, COLING 2008, AAAI 2008, ICDE 2008 |
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Eugene Agichtein is an
Assistant Professor in the Mathematics and
Computer Science Department at Emory
University, where he directs the Intelligent Information Access Lab.
Previously,
Eugene was a Postdoctoral Researcher in the
Text Mining, Search, and Navigation group at Microsoft Research,
working on text and web mining for information retrieval.
He received a Ph.D. in Computer Science from
Columbia
University in 2005, and
a B.S. in Engineering from The Cooper Union in 1998.
Eugene is a recipient of the “Best Student Paper” award
at the ICDE 2003 conference, and the “Best Paper Award” at the SIGMOD 2006
conference. Dr. Agichtein works on information
extraction, text mining, and user behavior modeling for information access,
management, and discovery from large text datasets such as the web, social
media, and scientific literature.
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